Speech recognition text entry for software applications

ABSTRACT

In embodiments of the present invention improved capabilities are described for a mobile environment speech processing facility. The present invention may provide for the entering of text into a software application resident on a mobile communication facility, where recorded speech may be presented by the user using the mobile communications facility&#39;s resident capture facility. Transmission of the recording may be provided through a wireless communication facility to a speech recognition facility, and may be accompanied by information related to the software application. Results may be generated utilizing the speech recognition facility that may be independent of structured grammar, and may be based at least in part on the information relating to the software application and the recording. The results may then be transmitted to the mobile communications facility, where they may be loaded into the software application. In addition, the speech recognition facility may be adapted based on usage.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of the following provisionalapplications, each of which is hereby incorporated by reference in itsentirety:

U.S. Provisional App. No. 60/893,600 filed Mar. 7, 2007; and

U.S. Provisional App. No. 60/976,050 filed Sep. 28, 2007.

BACKGROUND

1. Field

The present invention is related to speech recognition, and specificallyto speech recognition in association with a mobile communicationsfacility.

2. Description of the Related Art

Speech recognition, also known as automatic speech recognition, is theprocess of converting a speech signal to a sequence of words by means ofan algorithm implemented as a computer program. Speech recognitionapplications that have emerged over the last years include voice dialing(e.g., call home), call routing (e.g., I would like to make a collectcall), simple data entry (e.g., entering a credit card number), andpreparation of structured documents (e.g., a radiology report). Currentsystems are either not for mobile communication devices or utilizeconstraints, such as requiring a specified grammar, to provide real-timespeech recognition. The current invention provides a facility forunconstrained, mobile, real-time speech recognition.

SUMMARY

The current invention allows an individual with a mobile communicationsfacility to use speech recognition to enter text, such as into acommunications application, such as an SMS message, instant messenger,e-mail, or any other application, such as applications for gettingdirections, entering query word string into a search engine, commandsinto a navigation or map program, and a wide range of others.

In embodiments the present invention may provide for the entering oftext into a software application resident on a mobile communicationfacility, where recorded speech may be presented by the user using themobile communications facility's resident capture facility. Transmissionof the recording may be provided through a wireless communicationfacility to a speech recognition facility, and may be accompanied byinformation related to the software application. Results may begenerated utilizing the speech recognition facility that may beindependent of structured grammar, and may be based at least in part onthe information relating to the software application and the recording.The results may then be transmitted to the mobile communicationsfacility, where they may be loaded into the software application. Inembodiments, the user may be allowed to alter the results that arereceived from the speech recognition facility. In addition, the speechrecognition facility may be adapted based on usage.

In embodiments, the information relating to the software application mayinclude at least one of an identity of the application, an identity of atext box within the application, contextual information within theapplication, an identity of the mobile communication facility, anidentity of the user, and the like.

In embodiments, the step of generating the results may be based at leastin part on the information relating to the software application involvedin selecting at least one of a plurality of recognition models based onthe information relating to the software application and the recording,where the recognition models may include at least one of an acousticmodel, a pronunciation, a vocabulary, a language model, and the like,and at least one of a plurality of language models, wherein the at leastone of the plurality of language models may be selected based on theinformation relating to the software application and the recording. Inembodiments, the plurality of language models may be run at the sametime or in multiple passes in the speech recognition facility. Theselection of language models for subsequent passes may be based on theresults obtained in previous passes. The output of multiple passes maybe combined into a single result by choosing the highest scoring result,the results of multiple passes, and the like, where the merging ofresults may be at the word, phrase, or the like level.

In embodiments, adapting the speech recognition facility may be based onusage that includes at least one of adapting an acoustic model, adaptinga pronunciation, adapting a vocabulary, adapting a language model, andthe like. Adapting the speech recognition facility may include adaptingrecognition models based on usage data, where the process may be anautomated process, the models may make use of the recording, the modelsmay make use of words that are recognized, the models may make use ofthe information relating to the software application about action takenby the user, the models may be specific to the user or groups of users,the models may be specific to text fields with in the softwareapplication or groups of text fields within the software applications,and the like.

In embodiments, the step of allowing the user to alter the results mayinclude the user editing a text result using at least one of a keypad ora screen-based text correction mechanism, selecting from among aplurality of alternate choices of words contained in the results,selecting from among a plurality of alternate actions related to theresults, selecting among a plurality of alternate choices of phrasescontained in the results, selecting words or phrases to alter byspeaking or typing, positioning a cursor and inserting text at thecursor position by speaking or typing, and the like. In addition, thespeech recognition facility may include a plurality of recognitionmodels that may be adapted based on usage, including utilizing resultsaltered by the user, adapting language models based on usage fromresults altered by the user, and the like.

These and other systems, methods, objects, features, and advantages ofthe present invention will be apparent to those skilled in the art fromthe following detailed description of the preferred embodiment and thedrawings. All documents mentioned herein are hereby incorporated intheir entirety by reference.

BRIEF DESCRIPTION OF THE FIGURES

The invention and the following detailed description of certainembodiments thereof may be understood by reference to the followingfigures:

FIG. 1 depicts a block diagram of the mobile environment speechprocessing facility.

FIG. 2 depicts a block diagram of the automatic speech recognitionserver infrastructure architecture.

FIG. 3 depicts a block diagram of the application infrastructurearchitecture.

FIG. 4 depicts some of the components of the ASR Client.

FIG. 5 a depicts the process by which multiple language models may beused by the ASR engine.

FIG. 5 b depicts the process by which multiple language models may beused by the ASR engine for a navigation application embodiment.

FIG. 5 c depicts the process by which multiple language models may beused by the ASR engine for a messaging application embodiment.

FIG. 5 d depicts the process by which multiple language models may beused by the ASR engine for a content search application embodiment.

FIG. 5 e depicts the process by which multiple language models may beused by the ASR engine for a search application embodiment.

FIG. 5 f depicts the process by which multiple language models may beused by the ASR engine for a browser application embodiment.

FIG. 6 depicts the components of the ASR engine.

FIG. 7 depicts the layout and initial screen for the user interface.

FIG. 8 depicts a keypad layout for the user interface.

FIG. 9 depicts text boxes for the user interface.

FIG. 10 depicts a first example of text entry for the user interface.

FIG. 11 depicts a second example of text entry for the user interface.

FIG. 12 depicts a third example of text entry for the user interface.

FIG. 13 depicts speech entry for the user interface.

FIG. 14 depicts speech-result correction for the user interface.

FIG. 15 depicts a first example of navigating browser screen for theuser interface.

FIG. 16 depicts a second example of navigating browser screen for theuser interface.

FIG. 17 depicts packet types communicated between the client, router,and server at initialization and during a recognition cycle.

FIG. 18 depicts an example of the contents of a header.

FIG. 19 depicts the format of a status packet.

DETAILED DESCRIPTION

The current invention provides an unconstrained, real-time, mobileenvironment speech processing facility 100, as shown in FIG. 1, allowinga user with a mobile communications facility 120 to use speechrecognition to enter text into an application 112, such as acommunications application, such as an SMS message, IM message, e-mail,chat, blog, or the like, or any other kind of application, such as asocial network application, mapping application, application forobtaining directions, search engine, auction application, applicationrelated to music, travel, games, or other digital media, enterprisesoftware applications, word processing, presentation software, and thelike. In various embodiments, text obtained through the speechrecognition facility described herein may be entered into anyapplication or environment that takes text input.

In an embodiment of the invention, the user's 130 mobile communicationsfacility 120 may be a mobile phone, programmable through a standardprogramming language, such as Java, C, or C++. The mobile environmentspeech processing facility 100 may include a preloaded mobilecommunications facility 120. Or, the user 130 may download theapplication 112 to their mobile communications facility 120. Theapplication 112 may be for example a navigation application 112, a musicplayer, a music download service, a messaging application 112 such asSMS or email, a video player or search application 112, a local searchapplication 112, a mobile search application 112, a general internetbrowser or the like. There may also be multiple applications 112 loadedon the mobile communications facility 120 at the same time. The user 130may activate the mobile environment speech processing facility's 100user 130 interface software by starting a program included in the mobileenvironment speech processing facility 120 or activate it by performinga user 130 action, such as pushing a button or a touch screen to collectaudio into a domain application. The audio signal may then be recordedand routed over a network to the servers 110 of the mobile environmentspeech processing facility 100. The text output from the servers 110,representing the user's 130 spoken words, may then be routed back to theuser's 130 mobile communications facility 120 for display. Inembodiments, the user 130 may receive feedback from the mobileenvironment speech processing facility 100 on the quality of the audiosignal, for example, whether the audio signal has the right amplitude;whether the audio signal's amplitude is clipped, such as clipped at thebeginning or at the end; whether the signal was too noisy; or the like.

The user 130 may correct the returned text with the mobile phone'skeypad or touch screen navigation buttons. This process may occur inreal-time, creating an environment where a mix of speaking and typing isenabled in combination with other elements on the display. The correctedtext may be routed back to the servers 110, where the ASR Server 204Infrastructure 102 may use the corrections to help model how a user 130typically speaks, what words they use, how the user 130 tends to usewords, in what contexts the user 130 speaks, and the like. The user 130may speak or type into text boxes, with keystrokes routed back to theASR server 204. The core speech recognition engine 208 may includeautomated speech recognition (ASR), and may utilize a plurality ofmodels 218, such as acoustic models 220, pronunciations 222,vocabularies 224, language models 228, and the like, in the analysis andtranslation of user 130 inputs. Personal language models 228 may bebiased for first, last name in an address book, user's 130 location,phone number, past usage data, or the like. As a result of this dynamicdevelopment of user 130 speech profiles, the user 130 may be free fromconstraints on how to speak; there may be no grammatical constraintsplaced on the mobile user 130, such as having to say something in afixed domain. The user 130 may be able to say anything the user 130wants into the user's 130 mobile communications facility 120, allowingthe user 130 to utilize text messaging, searching, entering an address,or the like, and ‘speaking into’ the text field, rather than having totype everything.

In addition, the hosted servers 110 may be run as an application serviceprovider (ASP). This may allow the benefit of running data from multipleapplications 112 and users 130, combining them to make more effectiverecognition models 218. This may allow better adaptation to the user130, to the scenario, and to the application 112, based on usage.

FIG. 1 depicts an architectural block diagram for the mobile environmentspeech processing facility 100, including a mobile communicationsfacility 120 and hosted servers 110 The ASR client may provide thefunctionality of speech-enabled text entry to the application. The ASRserver infrastructure 102 may interface with the ASR client 118, in theuser's 130 mobile communications facility 120, via a data protocol, suchas a transmission control protocol (TCP) connection or the like. The ASRserver infrastructure 102 may also interface with the user database 104.The user database 104 may also be connected with the registration 108facility. The ASR server infrastructure 102 may make use of externalinformation sources 124 to provide information about words, sentences,and phrases that the user 130 is likely to speak. The application 112 inthe user's mobile communication facility 120 may also make use ofserver-side application infrastructure 122, also via a data protocol.The server-side application infrastructure 122 may provide content forthe applications, such as navigation information, music or videos todownload, search facilities for content, local, or general web search,and the like. The server-side application infrastructure 122 may alsoprovide general capabilities to the application such as translation ofHTML or other web-based markup into a form which is suitable for theapplication 112. Within the user's 130 mobile communications facility120, application code 114 may interface with the ASR client 118 via aresident software interface, such as Java, C, or C++. The applicationinfrastructure 122 may also interface with the user database 104, andwith other external application information sources 128 such as theWorld Wide Web 330, or with external application-specific content suchas navigation services, music, video, search services, and the like.

FIG. 2 depicts the architecture for the ASR server infrastructure 102,containing functional blocks for the ASR client 118, ASR router 202, ASRserver 204, ASR engine 208, recognition models 218, usage data 212,human transcription 210, adaptation process 214, external informationsources 124, and user 130 database 104. In a typical deploymentscenario, multiple ASR servers 204 may be connected to an ASR router202; many ASR clients 118 may be connected to multiple ASR routers 102,and network traffic load balancers may be presented between ASR clients118 and ASR routers 202. The ASR client 118 may present a graphical user130 interface to the user 130, and establishes a connection with the ASRrouter 202. The ASR client 118 may pass information to the ASR router202, including a unique identifier for the individual phone (client ID)that may be related to a user 130 account created during a subscriptionprocess, and the type of phone (phone ID). The ASR client 118 maycollect audio from the user 130. Audio may be compressed into a smallerformat. Compression may be a standard compression scheme used forhuman-human conversation, or a specific compression scheme optimized forspeech recognition. The user 130 may indicate that the user 130 wouldlike to perform recognition. Indication may be made by way of pressingand holding a button for the duration the user 130 is speaking.Indication may be made by way of pressing a button to indicate thatspeaking will begin, and the ASR client 118 may collect audio until itdetermines that the user 130 is done speaking, by determining that therehas been no speech within some pre-specified time period. Inembodiments, voice activity detection may be entirely automated withoutthe need for an initial key press, such as by voice trained command, byvoice command specified on the display of the mobile communicationsfacility 120, or the like.

The ASR client 118 may pass audio, or compressed audio, to the ASRrouter 202. The audio may be sent after all audio is collected orstreamed while the audio is still being collected. The audio may includeadditional information about the state of the ASR client 118 andapplication 112 in which this client is embedded. This additionalinformation, plus the client ID and phone ID, is the client stateinformation. This additional information may include an identifier forthe application; an identifier for the particular text field of theapplication; an identifier for content being viewed in the currentapplication, the URL of the current web page being viewed in a browserfor example; or words which are already entered into a current textfield. There may be information about what words are before and afterthe current cursor location, or alternatively, a list of words alongwith information about the current cursor location. This additionalinformation may also include other information available in theapplication 112 or mobile communication facility 120 which may behelpful in predicting what users 130 may speak into the application 112such as the current location of the phone, information about contentsuch as music or videos stored on the phone, history of usage of theapplication, time of day, and the like.

The ASR client 118 may wait for results to come back from the ASR router202. Results may be returned as word strings representing the system'shypothesis about the words, which were spoken. The result may includealternate choices of what may have been spoken, such as choices for eachword, choices for strings of multiple words, or the like. The ASR client118 may present words to the user 130, that appear at the current cursorposition in the text box, or shown to the user 130 as alternate choicesby navigating with the keys on the mobile communications facility 120.The ASR client 118 may allow the user 130 to correct text by using acombination of selecting alternate recognition hypotheses, navigating towords, seeing list of alternatives, navigating to desired choice,selecting desired choice; deleting individual characters, using somedelete key on the keypad or touch screen; deleting entire words one at atime; inserting new characters by typing on the keypad; inserting newwords by speaking; replacing highlighted words by speaking; or the like.The list of alternatives may be alternate words or strings of word, ormay make use of application constraints to provide a list of alternateapplication-oriented items such as songs, videos, search topics or thelike. The ASR client 118 may also give a user 130 a means to indicatethat the user 130 would like the application to take some action basedon the input text; sending the current state of the input text (acceptedtext) back to the ASR router 202 when the user 130 selects theapplication action based on the input text; logging various informationabout user 130 activity by keeping track of user 130 actions, such astiming and content of keypad or touch screen actions, or corrections,and periodically sending it to the ASR router 202; or the like.

The ASR router 202 may provide a connection between the ASR client 118and the ASR server 204. The ASR router 202 may wait for connectionrequests from ASR clients 118. Once a connection request is made, theASR router 202 may decide which ASR server 204 to use for the sessionfrom the ASR client 118. This decision may be based on the current loadon each ASR server 204; the best predicted load on each ASR server 204;client state information; information about the state of each ASR server204, which may include current recognition models 218 loaded on the ASRengine 208 or status of other connections to each ASR server 204;information about the best mapping of client state information to serverstate information; routing data which comes from the ASR client 118 tothe ASR server 204; or the like. The ASR router 202 may also route data,which may come from the ASR server 204, back to the ASR client 118.

The ASR server 204 may wait for connection requests from the ASR router202. Once a connection request is made, the ASR server 204 may decidewhich recognition models 218 to use given the client state informationcoming from the ASR router 202. The ASR server 204 may perform any tasksneeded to get the ASR engine 208 ready for recognition requests from theASR router 202. This may include pre-loading recognition models 218 intomemory, or doing specific processing needed to get the ASR engine 208 orrecognition models 218 ready to perform recognition given the clientstate information. When a recognition request comes from the ASR router202, the ASR server 204 may perform recognition on the incoming audioand return the results to the ASR router 202. This may includedecompressing the compressed audio information, sending audio to the ASRengine 208, getting results back from the ASR engine 208, optionallyapplying a process to alter the words based on the text and on theClient State Information (changing “five dollars” to $5 for example),sending resulting recognized text to the ASR router 202, and the like.The process to alter the words based on the text and on the Client StateInformation may depend on the application 112, for example applyingaddress-specific changes (changing “seventeen dunster street to” to “17dunster st.”) in a location-based application 112 such as navigation orlocal search, applying internet-specific changes (changing “yahoo dotcom” to “yahoo.com”) in a search application 112, and the like.

The ASR server 204 may log information to the usage data 212 storage.This logged information may include audio coming from the ASR router202, client state information, recognized text, accepted text, timinginformation, user 130 actions, and the like. The ASR server 204 may alsoinclude a mechanism to examine the audio data and decide that thecurrent recognition models 218 are not appropriate given thecharacteristics of the audio data and the client state information. Inthis case the ASR server 204 may load new or additional recognitionmodels 218, do specific processing needed to get ASR engine 208 orrecognition models 218 ready to perform recognition given the clientstate information and characteristics of the audio data, rerun therecognition based on these new models, send back information to the ASRrouter 202 based on the acoustic characteristics causing the ASR to sendthe audio to a different ASR server 204, and the like.

The ASR engine 208 may utilize a set of recognition models 218 toprocess the input audio stream, where there may be a number ofparameters controlling the behavior of the ASR engine 208. These mayinclude parameters controlling internal processing components of the ASRengine 208, parameters controlling the amount of processing that theprocessing components will use, parameters controlling normalizations ofthe input audio stream, parameters controlling normalizations of therecognition models 218, and the like. The ASR engine 208 may outputwords representing a hypothesis of what the user 130 said and additionaldata representing alternate choices for what the user 130 may have said.This may include alternate choices for the entire section of audio;alternate choices for subsections of this audio, where subsections maybe phrases (strings of one or more words) or words; scores related tothe likelihood that the choice matches words spoken by the user 130; orthe like. Additional information supplied by the ASR engine 208 mayrelate to the performance of the ASR engine 208.

The recognition models 218 may control the behavior of the ASR engine208. These models may contain acoustic models 220, which may control howthe ASR engine 208 maps the subsections of the audio signal to thelikelihood that the audio signal corresponds to each possible soundmaking up words in the target language. These acoustic models 220 may bestatistical models, Hidden Markov models, may be trained on transcribedspeech coming from previous use of the system (training data), multipleacoustic models with each trained on portions of the training data,models specific to specific users 130 or groups of users 130, or thelike. These acoustic models may also have parameters controlling thedetailed behavior of the models. The recognition models 218 may includeacoustic mappings, which represent possible acoustic transformationeffects, may include multiple acoustic mappings representing differentpossible acoustic transformations, and these mappings may apply to thefeature space of the ASR engine 208. The recognition models 218 mayinclude representations of the pronunciations 222 of words in the targetlanguage. These pronunciations 222 may be manually created by humans,derived through a mechanism which converts spelling of words to likelypronunciations, derived based on spoken samples of the word, and mayinclude multiple possible pronunciations for each word in the vocabulary224, multiple sets of pronunciations for the collection of words in thevocabulary 224, and the like. The recognition models 218 may includelanguage models 228, which represent the likelihood of various wordsequences that may be spoken by the user 130. These language models 228may be statistical language models, n-gram statistical language models,conditional statistical language models which take into account theclient state information, may be created by combining the effects ofmultiple individual language models, and the like. The recognitionmodels 218 may include multiple language models 228 which are used in avariety of combinations by the ASR engine 208. The multiple languagemodels 228 may include language models 228 meant to represent the likelyutterances of a particular user 130 or group of users 130. The languagemodels 228 may be specific to the application 112 or type of application112.

The multiple language models 228 may include language models 228designed to model words, phrases, and sentences used by people speakingdestinations for a navigation or local search application 112 or thelike. These multiple language models 228 may include language models 228about locations, language models 228 about business names, languagemodels 228 about business categories, language models 228 about pointsof interest, language models 228 about addresses, and the like. Each ofthese types of language models 228 may be general models which providebroad coverage for each of the particular type of ways of entering adestination or may be specific models which are meant to model theparticular businesses, business categories, points of interest, oraddresses which appear only within a particular geographic region.

The multiple language models 228 may include language models 228designed to model words, phrases, and sentences used by people speakinginto messaging applications 112. These language models 228 may includelanguage models 228 specific to addresses, headers, and content fieldsof a messaging application 112. These multiple language models 228 maybe specific to particular types of messages or messaging application 112types.

The multiple language models 228 may include language models 228designed to model words, phrases, and sentences used by people speakingsearch terms for content such as music, videos, games, and the like.These multiple language models 228 may include language models 228representing artist names, song names, movie titles, TV show, popularartists, and the like. These multiple language models 228 may bespecific to various types of content such as music or video category ormay cover multiple categories.

The multiple language models 228 may include language models 228designed to model words, phrases, and sentences used by people speakinggeneral search terms into a search application. The multiple languagemodels 228 may include language models 228 for particular types ofsearch including content search, local search, business search, peoplesearch, and the like.

The multiple language models 228 may include language models 228designed to model words, phrases, and sentences used by people speakingtext into a general internet browser. These multiple language models 228may include language models 228 for particular types of web pages ortext entry fields such as search, form filling, dates, times, and thelike.

Usage data 212 may be a stored set of usage data 212 from the users 130of the service that includes stored digitized audio that may becompressed audio; client state information from each audio segment;accepted text from the ASR client 118; logs of user 130 behavior, suchas key-presses; and the like. Usage data 212 may also be the result ofhuman transcription 210 of stored audio, such as words that were spokenby user 130, additional information such as noise markers, informationabout the speaker such as gender or degree of accent, or the like.

Human transcription 210 may be software and processes for a human tolisten to audio stored in usage data 212, and annotate data with wordswhich were spoken, additional information such as noise markers,truncated words, information about the speaker such as gender or degreeof accent, or the like. A transcriber may be presented with hypothesizedtext from the system or presented with accepted text from the system.The human transcription 210 may also include a mechanism to targettranscriptions to a particular subset of usage data 212. This mechanismmay be based on confidence scores of the hypothesized transcriptionsfrom the ASR server 204.

The adaptation process 214 may adapt recognition models 218 based onusage data 212. Another criterion for adaptation 214 may be to reducethe number of errors that the ASR engine 208 would have made on theusage data 212, such as by rerunning the audio through the ASR engine208 to see if there is a better match of the recognized words to whatthe user 130 actually said. The adaptation 214 techniques may attempt toestimate what the user 130 actually said from the annotations of thehuman transcription 210, from the accepted text, from other informationderived from the usage data 212, or the like. The adaptation 214techniques may also make use of client state information 514 to producerecognition models 218 that are personalized to an individual user 130or group of users 130. For a given user 130 or group of users 130, thesepersonalized recognition models 218 may be created from usage data 212for that user 130 or group, as well as data from users 130 outside ofthe group such as through collaborative-filtering techniques todetermine usage patterns from a large group of users 130. The adaptationprocess 214 may also make use of application information to adaptrecognition models 218 for specific domain applications 112 or textfields within domain applications 112. The adaptation process 214 maymake use of information in the usage data 212 to adapt multiple languagemodels 228 based on information in the annotations of the humantranscription 210, from the accepted text, from other informationderived from the usage data 212, or the like. The adaptation process 214may make use of external information sources 124 to adapt therecognition models 218. These external information sources 124 maycontain recordings of speech, may contain information about thepronunciations of words, may contain examples of words that users 130may speak into particular applications, may contain examples of phrasesand sentences which users 130 may speak into particular applications,and may contain structured information about underlying entities orconcepts that users 130 may speak about. The external informationsources 124 may include databases of location entities including cityand state names, geographic area names, zip codes, business names,business categories, points of interest, street names, street numberranges on streets, and other information related to locations anddestinations. These databases of location entities may include linksbetween the various entities such as which businesses and streets appearin which geographic locations and the like. The external information 124may include sources of popular entertainment content such as music,videos, games, and the like. The external information 124 may includeinformation about popular search terms, recent news headlines, or othersources of information which may help predict what users may speak intoa particular application 112. The external information sources 124 maybe specific to a particular application 112, group of applications 112,user 130, or group of users 130. The external information sources 124may include pronunciations of words that users may use. The externalinformation 124 may include recordings of people speaking a variety ofpossible words, phrases, or sentences. The adaptation process 214 mayinclude the ability to convert structured information about underlyingentities or concepts into words, phrases, or sentences which users 130may speak in order to refer to those entities or concepts. The adaptionprocess 214 may include the ability to adapt each of the multiplelanguage models 228 based on relevant subsets of the externalinformation sources 124 and usage data 212. This adaptation 214 oflanguage models 228 on subsets of external information source 124 andusage data 212 may include adapting geographic location-specificlanguage models 228 based on location entities and usage data 212 fromonly that geographic location, adapting application-specific languagemodels based on the particular application 112 type, adaptation 124based on related data or usages, or may include adapting 124 languagemodels 228 specific to particular users 130 or groups of users 130 onusage data 212 from just that user 130 or group of users 130.

The user database 104 may be updated by web registration 108 process, bynew information coming from the ASR router 202, by new informationcoming from the ASR server 204, by tracking application usagestatistics, or the like. Within the user database 104 there may be twoseparate databases, the ASR database and the user database 104. The ASRdatabase may contain a plurality of tables, such as asr_servers;asr_routers; asr_am (AM, profile name & min server count); asr_monitor(debugging), and the like. The user 130 database 104 may also contain aplurality of tables, such as a clients table including client ID, user130 ID, primary user 130 ID, phone number, carrier, phone make, phonemodel, and the like; a users 130 table including user 130 ID, developerpermissions, registration time, last activity time, activity countrecent AM ID, recent LM ID, session count, last session timestamp, AM ID(default AM for user 130 used from priming), and the like; a user 130preferences table including user 130 ID, sort, results, radius, savedsearches, recent searches, home address, city, state (for geocoding),last address, city, state (for geocoding), recent locations, city tostate map (used to automatically disambiguate one-to-many city/staterelationship) and the like; user 130 private table including user 130ID, first and last name, email, password, gender, type of user 130 (e.g.data collection, developer, VIP, etc), age and the like; user 130parameters table including user 130 ID, recognition server URL, proxyserver URL, start page URL, logging server URL, logging level, isLogging, is Developer, or the like; clients updates table used to sendupdate notices to clients, including client ID, last known version,available version, minimum available version, time last updated, timelast reminded, count since update available, count since last reminded,reminders sent, reminder count threshold, reminder time threshold,update URL, update version, update message, and the like; or othersimilar tables, such as application usage data 212 not related to ASR.

FIG. 3 depicts an example browser-based application infrastructurearchitecture 300 including the browser renderer 302, the browser proxy604, text-to-speech (TTS) server 308, TTS engine 310, speech awaremobile portal (SAMP) 312, text-box router 314, domain applications 312,scrapper 320, user 130 database 104, and the World Wide Web 330. Thebrowser renderer 302 may be a part of the application code 114 in theusers mobile communication facility 120 and may provide a graphical andspeech user 130 interface for the user 130 and display elements onscreen-based information coming from browser proxy 304. Elements mayinclude text elements, image elements, link elements, input elements,format elements, and the like. The browser renderer 302 may receiveinput from the user 130 and send it to the browser proxy 304. Inputs mayinclude text in a text-box, clicks on a link, clicks on an inputelement, or the like. The browser renderer 302 also may maintain thestack required for “Back” key presses, pages associated with each tab,and cache recently-viewed pages so that no reads from proxy are requiredto display recent pages (such as “Back”).

The browser proxy 304 may act as an enhanced HTML browser that issueshttp requests for pages, http requests for links, interprets HTML pages,or the like. The browser proxy 304 may convert user 130 interfaceelements into a form required for the browser renderer 302. The browserproxy 304 may also handle TTS requests from the browser renderer 302;such as sending text to the TTS server 308; receiving audio from the TTSserver 308 that may be in compressed format; sending audio to thebrowser renderer 302 that may also be in compressed format; and thelike.

Other blocks of the browser-based application infrastructure 300 mayinclude a TTS server 308, TTS engine 310, SAMP 312, user 130 database104 (previously described), the World Wide Web 330, and the like. TheTTS server 308 may accept TTS requests, send requests to the TTS engine310, receive audio from the TTS engine 310, send audio to the browserproxy 304, and the like. The TTS engine 310 may accept TTS requests,generate audio corresponding to words in the text of the request, sendaudio to the TTS server 308, and the like. The SAMP 312 may handleapplication requests from the browser proxy 304, behave similar to a webapplication 330, include a text-box router 314, include domainapplications 318, include a scrapper 320, and the like. The text-boxrouter 314 may accept text as input, similar to a search engine's searchbox, semantically parsing input text using geocoding, key word andphrase detection, pattern matching, and the like. The text-box router314 may also route parse requests accordingly to appropriate domainapplications 318 or the World Wide Web 330. Domain applications 318 mayrefer to a number of different domain applications 318 that may interactwith content on the World Wide Web 330 to provide application-specificfunctionality to the browser proxy. And finally, the scrapper 320 mayact as a generic interface to obtain information from the World Wide Web330 (e.g., web services, SOAP, RSS, HTML, scrapping, and the like) andformatting it for the small mobile screen.

FIG. 4 depicts some of the components of the ASR Client 114. The ASRclient 114 may include an audio capture 402 component which may wait forsignals to begin and end recording, interacts with the built-in audiofunctionality on the mobile communication facility 120, interact withthe audio compression 408 component to compress the audio signal into asmaller format, and the like. The audio capture 402 component mayestablish a data connection over the data network using the servercommunications component 410 to the ASR server infrastructure 102 usinga protocol such as TCP or HTTP. The server communications 410 componentmay then wait for responses from the ASR server infrastructure 102indicated words which the user may have spoken. The correction interface404 may display words, phrases, sentences, or the like, to the user, 130indicating what the user 130 may have spoken and may allow the user 130to correct or change the words using a combination of selectingalternate recognition hypotheses, navigating to words, seeing list ofalternatives, navigating to desired choice, selecting desired choice;deleting individual characters, using some delete key on the keypad ortouch screen; deleting entire words one at a time; inserting newcharacters by typing on the keypad; inserting new words by speaking;replacing highlighted words by speaking; or the like. Audio compression408 may compress the audio into a smaller format using audio compressiontechnology built into the mobile communication facility 120, or by usingits own algorithms for audio compression. These audio compression 408algorithms may compress the audio into a format which can be turned backinto a speech waveform, or may compress the audio into a format whichcan be provided to the ASR engine 208 directly or uncompressed into aformat which may be provided to the ASR engine 208. Servercommunications 410 may use existing data communication functionalitybuilt into the mobile communication facility 120 and may use existingprotocols such as TCP, HTTP, and the like.

FIG. 5 a depicts the process 500 a by which multiple language models maybe used by the ASR engine. For the recognition of a given utterance, afirst process 504 may decide on an initial set of language models 228for the recognition. This decision may be made based on the set ofinformation in the client state information 514, including applicationID, user ID, text field ID, current state of application 112, orinformation such as the current location of the mobile communicationfacility 120. The ASR engine 208 may then run 508 using this initial setof language models 228 and a set of recognition hypotheses created basedon this set of language models 228. There may then be a decision process510 to decide if additional recognition passes 508 are needed withadditional language models 228. This decision 510 may be based on theclient state information 514, the words in the current set ofrecognition hypotheses, confidence scores from the most recentrecognition pass, and the like. If needed, a new set of language models228 may be determined 518 based on the client state information 514 andthe contents of the most recent recognition hypotheses and another passof recognition 508 made by the ASR engine 208. Once complete, therecognition results may be combined to form a single set of words andalternates to pass back to the ASR client 118.

FIG. 5 b depicts the process 500 b by which multiple language models 228may be used by the ASR engine 208 for an application 112 which allowsspeech input 502 about locations, such as a navigation, local search, ordirectory assistance application 112. For the recognition of a givenutterance, a first process 522 may decide on an initial set of languagemodels 228 for the recognition. This decision may be made based on theset of information in the client state information 524, includingapplication ID, user ID, text field ID, current state of application112, or information such as the current location of the mobilecommunication facility 120. This client state information may alsoinclude favorites or an address book from the user 130 and may alsoinclude usage history for the application 112. The decision about theinitial set of language models 228 may be based on likely target citiesfor the query 522. The initial set of language models 228 may includegeneral language models 228 about business names, business categories,city and state names, points of interest, street addresses, and otherlocation entities or combinations of these types of location entities.The initial set of language models 228 may also include models 228 foreach of the types of location entities specific to one or moregeographic regions, where the geographic regions may be based on thephone's current geographic location, usage history for the particularuser 130, or other information in the navigation application 112 whichmay be useful in predicting the likely geographic area the user 130 maywant to enter into the application 112. The initial set of languagemodels 228 may also include language models 228 specific to the user 130or group to which the user 130 belongs. The ASR engine 208 may then run508 using this initial set of language models 228 and a set ofrecognition hypotheses created based on this set of language models 228.There may then be a decision process 510 to decide if additionalrecognition passes 508 are needed with additional language models 228.This decision 510 may be based on the client state information 524, thewords in the current set of recognition hypotheses, confidence scoresfrom the most recent recognition pass, and the like. This decision mayinclude determining the likely geographic area of the utterance andcomparing that to the assumed geographic area or set of areas in theinitial language models 228. This determining the likely geographic areaof the utterance may include looking for words in the hypothesis or setof hypotheses, which may correspond to a geographic region. These wordsmay include names for cities, states, areas and the like or may includea string of words corresponding to a spoken zip code. If needed, a newset of language models 228 may be determined 528 based on the clientstate information 524 and the contents of the most recent recognitionhypotheses and another pass of recognition 508 made by the ASR engine208. This new set of language models 228 may include language models 228specific to a geographic region determined from a hypothesis or set ofhypotheses from the previous recognition pass Once complete, therecognition results may be combined 512 to form a single set of wordsand alternates to pass back 520 to the ASR client 118.

FIG. 5 c depicts the process 500 c by which multiple language models 228may be used by the ASR engine 208 for a messaging application 112 suchas SMS, email, instant messaging, and the like, for speech input 502.For the recognition of a given utterance, a first process 532 may decideon an initial set of language models 228 for the recognition. Thisdecision may be made based on the set of information in the client stateinformation 534, including application ID, user ID, text field ID, orcurrent state of application 112. This client state information mayinclude an address book or contact list for the user, contents of theuser's messaging inbox and outbox, current state of any text entered sofar, and may also include usage history for the application 112. Thedecision about the initial set of language models 228 may be based onthe user 130, the application 112, the type of message, and the like.The initial set of language models 228 may include general languagemodels 228 for messaging applications 112, language models 228 forcontact lists and the like. The initial set of language models 228 mayalso include language models 228 specific to the user 130 or group towhich the user 130 belongs. The ASR engine 208 may then run 508 usingthis initial set of language models 228 and a set of recognitionhypotheses created based on this set of language models 228. There maythen be a decision process 510 to decide if additional recognitionpasses 508 are needed with additional language models 228. This decision510 may be based on the client state information 534, the words in thecurrent set of recognition hypotheses, confidence scores from the mostrecent recognition pass, and the like. This decision may includedetermining the type of message entered and comparing that to theassumed type of message or types of messages in the initial languagemodels 228. If needed, a new set of language models 228 may bedetermined 538 based on the client state information 534 and thecontents of the most recent recognition hypotheses and another pass ofrecognition 508 made by the ASR engine 208. This new set of languagemodels 228 may include language models specific to the type of messagesdetermined from a hypothesis or set of hypotheses from the previousrecognition pass Once complete, the recognition results may be combined512 to form a single set of words and alternates to pass back 520 to theASR client 118.

FIG. 5 d depicts the process 500 d by which multiple language models 228may be used by the ASR engine 208 for a content search application 112such as music download, music player, video download, video player, gamesearch and download, and the like, for speech input 502. For therecognition of a given utterance, a first process 542 may decide on aninitial set of language models 228 for the recognition. This decisionmay be made based on the set of information in the client stateinformation 544, including application ID, user ID, text field ID, orcurrent state of application 112. This client state information mayinclude information about the user's content and playlists, either onthe client itself or stored in some network-based storage, and may alsoinclude usage history for the application 112. The decision about theinitial set of language models 228 may be based on the user 130, theapplication 112, the type of content, and the like. The initial set oflanguage models 228 may include general language models 228 for search,language models 228 for artists, composers, or performers, languagemodels 228 for specific content such as song and album names, movie andTV show names, and the like. The initial set of language models 228 mayalso include language models 228 specific to the user 130 or group towhich the user 130 belongs. The ASR engine 208 may then run 508 usingthis initial set of language models 228 and a set of recognitionhypotheses created based on this set of language models 228. There maythen be a decision process 510 to decide if additional recognitionpasses 508 are needed with additional language models 228. This decision510 may be based on the client state information 544, the words in thecurrent set of recognition hypotheses, confidence scores from the mostrecent recognition pass, and the like. This decision may includedetermining the type of content search and comparing that to the assumedtype of content search in the initial language models 228. If needed, anew set of language models 228 may be determined 548 based on the clientstate information 544 and the contents of the most recent recognitionhypotheses and another pass of recognition 508 made by the ASR engine208. This new set of language models 228 may include language models 228specific to the type of content search determined from a hypothesis orset of hypotheses from the previous recognition pass Once complete, therecognition results may be combined 512 to form a single set of wordsand alternates to pass back 520 to the ASR client 118.

FIG. 5 e depicts the process 500 e by which multiple language models 228may be used by the ASR engine 208 for a search application 112 such asgeneral web search, local search, business search, and the like, forspeech input 502. For the recognition of a given utterance, a firstprocess 552 may decide on an initial set of language models 228 for therecognition. This decision may be made based on the set of informationin the client state information 554, including application ID, user ID,text field ID, or current state of application 112. This client stateinformation may include information about the phone's location, and mayalso include usage history for the application 112. The decision aboutthe initial set of language models 228 may be based on the user 130, theapplication 112, the type of search, and the like. The initial set oflanguage models 228 may include general language models 228 for search,language models 228 for different types of search such as local search,business search, people search, and the like. The initial set oflanguage models 228 may also include language models 228 specific to theuser or group to which the user belongs. The ASR engine 208 may then run508 using this initial set of language models 228 and a set ofrecognition hypotheses created based on this set of language models 228.There may then be a decision process 510 to decide if additionalrecognition passes 508 are needed with additional language models 228.This decision 510 may be based on the client state information 554, thewords in the current set of recognition hypotheses, confidence scoresfrom the most recent recognition pass, and the like. This decision mayinclude determining the type of search and comparing that to the assumedtype of search in the initial language models. If needed, a new set oflanguage models 228 may be determined 558 based on the client stateinformation 554 and the contents of the most recent recognitionhypotheses and another pass of recognition 508 made by the ASR engine208. This new set of language models 228 may include language models 228specific to the type of search determined from a hypothesis or set ofhypotheses from the previous recognition pass. Once complete, therecognition results may be combined 512 to form a single set of wordsand alternates to pass back 520 to the ASR client 118.

FIG. 5 f depicts the process 500 f by which multiple language models 228may be used by the ASR engine 208 for a general browser as amobile-specific browser or general internet browser for speech input502. For the recognition of a given utterance, a first process 562 maydecide on an initial set of language models 228 for the recognition.This decision may be made based on the set of information in the clientstate information 564, including application ID, user ID, text field ID,or current state of application 112. This client state information mayinclude information about the phone's location, the current web page,the current text field within the web page, and may also include usagehistory for the application 112. The decision about the initial set oflanguage models 228 may be based on the user 130, the application 112,the type web page, type of text field, and the like. The initial set oflanguage models 228 may include general language models 228 for search,language models 228 for date and time entry, language models 228 fordigit string entry, and the like. The initial set of language models 228may also include language models 228 specific to the user 130 or groupto which the user 130 belongs The ASR engine 208 may then run 508 usingthis initial set of language models 228 and a set of recognitionhypotheses created based on this set of language models 228. There maythen be a decision process 510 to decide if additional recognitionpasses 508 are needed with additional language models 228. This decision510 may be based on the client state information 564, the words in thecurrent set of recognition hypotheses, confidence scores from the mostrecent recognition pass, and the like. This decision may includedetermining the type of entry and comparing that to the assumed type ofentry in the initial language models 228. If needed, a new set oflanguage models 228 may be determined 568 based on the client stateinformation 564 and the contents of the most recent recognitionhypotheses and another pass of recognition 508 made by the ASR engine208. This new set of language models 228 may include language models 228specific to the type of entry determined from a hypothesis or set ofhypotheses from the previous recognition pass Once complete, therecognition results may be combined 512 to form a single set of wordsand alternates to pass back 520 to the ASR client 118.

The process to combine recognition output may make use of multiplerecognition hypotheses from multiple recognition passes. These multiplehypotheses may be represented as multiple complete sentences or phrases,or may be represented as a directed graph allowing multiple choices foreach word. The recognition hypotheses may include scores representinglikelihood or confidence of words, phrases, or sentences. Therecognition hypotheses may also include timing information about whenwords and phrases start and stop. The process to combine recognitionoutput may choose entire sentences or phrases from the sets ofhypotheses or may construct new sentences or phrases by combining wordsor fragments of sentences or phrases from multiple hypotheses. Thechoice of output may depend on the likelihood or confidence scores andmay take into account the time boundaries of the words and phrases.

FIG. 6 shows the components of the ASR engine 208. The components mayinclude signal processing 602 which may process the input speech eitheras a speech waveform or as parameters from a speech compressionalgorithm and create representations which may be used by subsequentprocessing in the ASR engine 208. Acoustic scoring 604 may use acousticmodels 220 to determine scores for a variety of speech sounds forportions of the speech input. The acoustic models 220 may be statisticalmodels and the scores may be probabilities. The search 608 component maymake use of the score of speech sounds from the acoustic scoring 602 andusing pronunciations 222, vocabulary 224, and language models 228, findthe highest scoring words, phrases, or sentences and may also producealternate choices of words, phrases, or sentences.

FIG. 7 shows an example of how the user 130 interface layout and initialscreen 700 may look on a user's 130 mobile communications facility 120.The layout, from top to bottom, may include a plurality of components,such as a row of navigable tabs, the current page, soft-key labels atthe bottom that can be accessed by pressing the left or right soft-keyson the phone, a scroll-bar on the right that shows vertical positioningof the screen on the current page, and the like. The initial screen maycontain a text-box with a “Search” button, choices of which domainapplications 318 to launch, a pop-up hint for first-time users 130, andthe like. The text box may be a shortcut that users 130 can enter into,or speak into, to jump to a domain application 318, such as “Restaurantsin Cambridge” or “Send a text message to Joe”. When the user 130 selectsthe “Search” button, the text content is sent. Application choices maysend the user 130 to the appropriate application when selected. Thepopup hint 1) tells the user 130 to hold the green TALK button to speak,and 2) gives the user 130 a suggestion of what to say to try the systemout. Both types of hints may go away after several uses.

Although there are mobile phones with full alphanumeric keyboards, mostmass-market devices are restricted to the standard telephone keypad 802,such as shown in FIG. 8. Command keys may include a “TALK”, orgreen-labeled button, which may be used to make a regular voice-basedphone call; an “END” button which is used to terminate a voice-basedcall or end an application 112 and go back to the phone's main screen; afive-way control joystick that users 130 may employ to move up, down,left, and right, or select by pressing on the center button (labeled“MENU/OK” in FIG. 8); two soft-key buttons that may be used to selectthe labels at the bottom of the screen; a back button which is used togo back to the previous screen in any application; a delete button usedto delete entered text that on some phones, such as the one pictured inFIG. 8, the delete and back buttons are collapsed into one; and thelike.

FIG. 9 shows text boxes in a navigate-and-edit mode. A text box iseither in navigate mode or edit mode 900. When in navigate mode 902, nocursor or a dim cursor is shown and ‘up/down’, when the text box ishighlighted, moves to the next element on the browser screen. Forexample, moving down would highlight the “search” box. The user 130 mayenter edit mode from navigate mode 902 on any of a plurality of actions;including pressing on center joystick; moving left/right in navigatemode; selecting “Edit” soft-key; pressing any of the keys 0-9, whichalso adds the appropriate letter to the text box at the current cursorposition; and the like. When in edit mode 904, a cursor may be shown andthe left soft-key may be “Clear” rather than “Edit.” The current shiftmode may be also shown in the center of the bottom row. In edit mode904, up and down may navigate within the text box, although users 130may also navigate out of the text box by navigating past the first andlast rows. In this example, pressing up would move the cursor to thefirst row, while pressing down instead would move the cursor out of thetext box and highlight the “search” box instead. The user 130 may holdthe navigate buttons down to perform multiple repeated navigations. Whenthe same key is held down for an extended time, four seconds forexample, navigation may be sped up by moving more quickly, for instance,times four in speed. As an alternative, navigate mode 902 may be removedso that when the text box is highlighted, a cursor may be shown. Thismay remove the modality, but then requires users 130 to move up and downthrough each line of the text box when trying to navigate past the textbox.

Text may be entered in the current cursor position in multi-tap mode, asshown in FIGS. 10, 11, and 12. As an example, pressing “2” once may bethe same as entering “a”, pressing “2” twice may be the same as entering“b”, pressing “2” three times may be the same as entering “c”, andpressing “2” 4 times may be the same as entering “2”. The direction keysmay be used to reposition the cursor. Back, or delete on some phones,may be used to delete individual characters. When Back is held down,text may be deleted to the beginning of the previous recognition result,then to the beginning of the text. Capitalized letters may be entered bypressing the “*” key which may put the text into capitalization mode,with the first letter of each new word capitalized. Pressing “*” againputs the text into all-caps mode, with all new entered letterscapitalized. Pressing “*” yet again goes back to lower case mode whereno new letters may be capitalized. Numbers may be entered either bypressing a key repeatedly to cycle through the letters to the number, orby going into numeric mode. The menu soft-key may contain a “Numbers”option which may put the cursor into numeric mode. Alternatively,numeric mode may be accessible by pressing “*” when cyclingcapitalization modes. To switch back to alphanumeric mode, the user 130may again select the Menu soft-key which now contains an “Alpha” option,or by pressing “*”. Symbols may be entered by cycling through the “1”key, which may map to a subset of symbols, or by bringing up the symboltable through the Menu soft-key. The navigation keys may be used totraverse the symbol table and the center OK button used to select asymbol and insert it at the current cursor position.

FIG. 13 provides examples of speech entry 1300, and how it is depictedon the user 130 interface. When the user 130 holds the TALK button tobegin speaking, a popup may appear informing the user 130 that therecognizer is listening 1302. In addition, the phone may either vibrateor play a short beep to cue the user 130 to begin speaking. When theuser 130 is finished speaking and releases the TALK button, the popupstatus may show “Working” 1004 with a spinning indicator. The user 130may cancel a processing recognition by pressing a button on the keypador touch screen, such as “Back” or a directional arrow. Finally, whenthe result is received from the ASR server 204, the text box may bepopulated 1008.

When the user 130 presses left or right to navigate through the textbox, alternate results 1402 for each word may be shown in gray below thecursor for a short time, such as 1.7 seconds. After that period, thegray alternates disappear, and the user 130 may have to move left orright again to get the box. If the user 130 presses down to navigate tothe alternates while it is visible, then the current selection in thealternates may be highlighted, and the words that will be replaced inthe original sentence may be highlighted in red 1404. The image on thebottom left of FIG. 14 shows a case where two words in the originalsentence will be replaced 1408. To replace the text with the highlightedalternate, the user 130 may press the center OK key. When the alternatelist is shown in red 1408 after the user 130 presses down to choose it,the list may become hidden and go back to normal cursor mode if there isno activity after some time, such as 5 seconds. When the alternate listis shown in red, the user 130 may also move out of it by moving up ordown past the top or bottom of the list, in which case the normal cursoris shown with no gray alternates box. When the alternate list is shownin red, the user 130 may navigate the text by words by moving left andright. For example, when “nobel” is highlighted 1404, moving right wouldhighlight “bookstore” and show its alternate list instead.

When the user 130 navigates to a new screen, the “Back” key may be usedto go back to the previous screen. As shown in FIG. 15, if the user 130presses “Back” after looking through the search results, the screen onthe left is shown 1502. When the user 130 navigates to a new page fromthe home page, a new tab may be automatically inserted to the right ofthe “home” tab, as shown in FIG. 16. Unless the user 130 is in a textbox, tabs can be navigated by pressing left or right keys. The user 130may also move to the top of the screen and select the tab itself beforemoving left or right. When the tab is highlighted, the user 130 may alsoselect the left soft-key to remove the current tab and screen. As analternative, tabs may show icons instead of names as pictured, tabs maybe shown at the bottom of the screen, the initial screen may bepre-populated with tabs, selection of an item from the home page maytake the user 130 to an existing tab instead of a new one, and tabs maynot be selectable by moving to the top of the screen and tabs may not beremovable by the user 130, and the like.

As shown in FIG. 2, there is communication between the ASR client 118,ASR router 202, and ASR server 204. These communications may be subjectto specific protocols. In these protocols, the ASR client 118, whenprompted by user 130, records audio and sends it to the ASR router 202.Received results from the ASR router 202 are displayed for the user 130.The user 130 may send user 130 entries to ASR router 202 for any textentry. The ASR router 202 sends audio to the appropriate ASR server 204,depending on the user 130 profile represented by the client ID and CPUload on ASR servers 204, then sends the results from the ASR server 204back to the ASR client 118. The ASR router 202 re-routes the data if theASR server 204 indicates a mismatched user 130 profile. The ASR router202 sends to the ASR server 204 any user 130 text inputs for editing.The ASR server 204 receives audio from ASR router 202 and performsrecognition. Results are returned to the ASR router 202. The ASR server204 alerts the ASR router 202 if the user's 130 speech no longer matchesthe user's 130 predicted user 130 profile, and the ASR router 202handles the appropriate re-route. The ASR server 204 also receivesuser-edit accepted text results from the ASR router 202.

FIG. 17 shows an illustration of the packet types that are communicatedbetween the ASR client 118, ASR router 202, and server 204 atinitialization and during a recognition cycle. During initialization, aconnection is requested, with the connection request going from ASRclient 118 to the ASR router 202 and finally to the ASR server 204. Aready signal is sent back from the ASR servers 204 to the ASR router 202and finally to the ASR client 118. During the recognition cycle, awaveform is input at the ASR client 118 and routed to the ASR servers204. Results are then sent back out to the ASR client 118, where theuser 130 accepts the returned text, sent back to the ASR servers 104. Aplurality of packet types may be utilized during these exchanges, suchas PACKET_WAVEFORM=1, packet is waveform; PACKET_TEXT=2, packet is text;PACKET_END_OF_STREAM=3, end of waveform stream; PACKET_IMAGE=4, packetis image; PACKET_SYNCLIST=5, syncing lists, such as email lists;PACKET_CLIENT_PARAMETERS=6, packet contains parameter updates forclient; PACKET_ROUTER_CONTROL=7, packet contains router controlinformation; PACKET_MESSAGE=8, packet contains status, warning or errormessage; PACKET_IMAGE_REQUEST=9, packet contains request for an image oricon; or the like. In addition, each message may have a header, such asshown in FIG. 18. All multi-byte words are in big-endian format.

As shown in FIG. 17, initialization may be sent from the ASR client 118,through the ASR router 202, to the ASR server 204. The ASR client 118may open a connection with the ASR router 202 by sending its Client ID.The ASR router 202 in turn looks up the ASR client's 118 most recentacoustic model 220 (AM) and language model 228 (LM) and connects to anappropriate ASR server 204. The ASR router 202 stores that connectionuntil the ASR client 118 disconnects or the Model ID changes. The packetformat for initialization may have a specific format, such as Packettype=TEXT, Data=ID:<client id string> ClientVersion: <client versionstring>, Protocol:<protocol id string> NumReconnects: <# attempts clienthas tried reconnecting to socket>, or the like. The communications pathfor initialization may be (1) Client sends Client ID to ASR router 202,(2) ASR router 202 forwards to ASR a modified packet: ModifiedData=<client's original packet data> SessionCount: <session countstring> SpeakerID: <user id sting>\0, and (3) resulting state: ASR isnow ready to accept utterance(s) from the ASR client 118, ASR router 202maintains client's ASR connection.

As shown in FIG. 17, a ready packet may be sent back to the ASR client118 from the ASR servers 204. The packet format for packet ready mayhave a specific format, such as Packet type=TEXT, Data=Ready\0, and thecommunications path may be (1) ASR sends Ready router and (2) ASR router202 forwards Ready packet to ASR client 118.

As shown in FIG. 17, a field ID packet containing the name of theapplication and text field within the application may be sent from theASR client 118 to the ASR servers 204. This packet is sent as soon asthe user 130 pushes the TALK button to begin dictating one utterance.The ASR servers 204 may use the field ID information to selectappropriate recognition models 142 for the next speech recognitioninvocation. The ASR router 202 may also use the field ID information toroute the current session to a different ASR server 204. The packetformat for the field ID packet may have a specific format, such asPacket type=TEXT; Data=FieldID; <type> <url><form element name>, forbrowsing mobile web pages; Data=FieldID: message, for SMS text box; orthe like. The connection path may be (1) ASR client 118 sends Field IDto ASR router 202 and (2) ASR router 202 forwards to ASR for logging.

As shown in FIG. 17, a waveform packet may be sent from the ASR client118 to the ASR servers 204. The ASR router 202 sequentially streamsthese waveform packets to the ASR server 204. If the ASR server 204senses a change in the Model ID, it may send the ASR router 202 aROUTER_CONTROL packet containing the new Model ID. In response, the ASRrouter 202 may reroute the waveform by selecting an appropriate ASR andflagging the waveform such that the new ASR server 204 will not performadditional computation to generate another Model ID. The ASR router 202may also re-route the packet if the ASR server's 204 connection drops ortimes out. The ASR router 202 may keep a cache of the most recentutterance, session information such as the client ID and the phone ID,and corresponding FieldID, in case this happens. The packet format forthe waveform packet may have a specific format, such as Packettype=WAVEFORM; Data=audio; with the lower 16 bits of flags set tocurrent Utterance ID of the client. The very first part of WAVEFORMpacket may determine the waveform type, currently only supporting AMR orQCELP, where “#!AMR\n” corresponds to AMR and “RIFF” corresponds toQCELP. The connection path may be (1) ASR client 118 sends initial audiopacket (referred to as the BOS, or beginning of stream) to the ASRrouter 202, (2) ASR router 202 continues streaming packets (regardlessof their type) to the current ASR until one of the following eventsoccur: (a) ASR router 202 receives packet type END_OF_STREAM, signalingthat this is the last packet for the waveform, (b) ASR disconnects ortimes out, in which case ASR router 202 finds new ASR, repeats abovehandshake, sends waveform cache, and continues streaming waveform fromclient to ASR until receives END_OF_STREAM, (c) ASR sends ROUTER_CONTROLto ASR router 202 instructing the ASR router 202 that the Model ID forthat utterance has changed, in which case the ASR router 202 behaves asin ‘b’, (d) ASR client 118 disconnects or times out, in which case thesession is closed, or the like. If the recognizer times out ordisconnects after the waveform is sent then the ASR router 202 mayconnect to a new ASR.

As shown in FIG. 17, a request model switch for utterance packet may besent from the ASR server 204 to the ASR router 202. This packet may besent when the ASR server 204 needs to flag that its user 130 profiledoes not match that of the utterance, i.e. Model ID for the utteranceshas changed. The packet format for the request model switch forutterance packet may have a specific format, such as Packettype=ROUTER_CONTROL; Data=SwitchModelID: AM=<integer> LM=<integer>SessionID=<integer> UttID=<integer>. The communication may be (1) ASRserver 204 sends control packet to ASR router 202 after receiving thefirst waveform packet, and before sending the results packet, and (2)ASR router 202 then finds an ASR which best matches the new Model ID,flags the waveform data such that the new ASR server 204 will not sendanother SwitchModelID packet, and resends the waveform. In addition,several assumptions may be made for this packet, such as the ASR server204 may continue to read the waveform packet on the connection, send aAlternate String or SwitchModelID for every utterance with BOS, and theASR router 202 may receive a switch model id packet, it sets the flagsvalue of the waveform packets to <flag value> & 0x8000 to notify ASRthat this utterance's Model ID does not need to be checked.

As shown in FIG. 17, a done packet may be sent from the ASR server 204to the ASR router 202. This packet may be sent when the ASR server 204has received the last audio packet, such as type END_OF_STREAM. Thepacket format for the done packet may have a specific format, such asPacket type=TEXT; with the lower 16 bits of flags set to Utterance IDand Data=Done\0. The communications path may be (1) ASR sends done toASR router 202 and (2) ASR router 202 forwards to ASR client 118,assuming the ASR client 118 only receives one done packet per utterance.

As shown in FIG. 17, an utterance results packet may be sent from theASR server 204 to the ASR client 118. This packet may be sent when theASR server 204 gets a result from the ASR engine 208. The packet formatfor the utterance results packet may have a specific format, such asPacket type=TEXT, with the lower 16 bits of flags set to Utterance IDand Data=ALTERNATES: <utterance result string>. The communications pathmay be (1) ASR sends results to ASR router 202 and (2) ASR router 202forwards to ASR client 118. The ASR client 118 may ignore the results ifthe Utterance ID does not match that of the current recognition

As shown in FIG. 17, an accepted text packet may be sent from the ASRclient 118 to the ASR server 204. This packet may be sent when the user130 submits the results of a text box, or when the text box loosesfocus, as in the API, so that the recognizer can adapt to correctedinput as well as fully-texted input. The packet format for the acceptedtext packet may have a specific format, such as Packet type=TEXT, withthe lower 16 bits of flags set to most recent Utterance ID, withData=Accepted_Text: <accepted utterance string>. The communications pathmay be (1) ASR client 118 sends the text submitted by the user 130 toASR router 202 and (2) ASR router 202 forwards to ASR server 204 whichrecognized results, where <accepted utterance string> contains the textstring entered into the text box. In embodiments, other logginginformation, such as timing information and user 130 editing keystrokeinformation may also be transferred.

Router control packets may be sent between the ASR client 118, ASRrouter 202, and ASR servers 204, to help control the ASR router 202during runtime. One of a plurality of router control packets may be aget router status packet. The packet format for the get router statuspacket may have a specific format, such as Packet type=ROUTER_CONTROL,with Data=GetRouterStatus\0. The communication path may be (1) entitysends this packet to the ASR router 202 and (2) ASR router 202 mayrespond with a status packet with a specific format, such as the format1900 shown in FIG. 19.

Another of a plurality of router control packets may be a busy out ASRserver packet. The packet format for the busy out ASR server packet mayhave a specific format, such as Packet type=ROUTER_CONTROL, withData=BusyOutASRServer: <ASR Server ID>\0. Upon receiving the busy outASR server packet, the ASR router 202 may continue to finish up theexisting sessions between the ASR router 202 and the ASR server 204identified by the <ASR Server ID>, and the ASR router 202 may not starta new session with the said ASR server 204. Once all existing sessionsare finished, the ASR router 202 may remove the said ASR server 204 fromits ActiveServer array. The communication path may be (1) entity sendsthis packet to the ASR router 202 and (2) ASR router 202 responds withACK packet with the following format: Packet type=TEXT, and Data=ACK\0.

Another of a plurality of router control packets may be an immediatelyremove ASR server packet. The packet format for the immediately removeASR server packet may have a specific format, such as Packettype=ROUTER_CONTROL, with Data=RemoveASRServer: <ASR Server ID>\0. Uponreceiving the immediately remove ASR server packet, the ASR router 202may immediately disconnect all current sessions between the ASR router202 and the ASR server 204 identified by the <ASR Server ID>, and theASR router 202 may also immediately remove the said ASR server 204 fromits Active Server array. The communication path may be (1) entity sendsthis packet to the ASR router 202 and (2) ASR router 202 responds withACK packet with the following format: Packet type=TEXT, and Data=ACK\0.

Another of a plurality of router control packets may be an add of an ASRserver 204 to the router packet. When an ASR server 204 is initiallystarted, it may send the router(s) this packet. The ASR router 202 inturn may add this ASR server 204 to its Active Server array afterestablishing this ASR server 204 is indeed functional. The packet formatfor the add an ASR server 204 to the ASR router 202 may have a specificformat, such as Packet type=ROUTER_CONTROL, with Data=AddASRServer:ID=<server id> IP=<server ip address> PORT=<server port> AM=<server AMinteger> LM=<server LM integer> NAME=<server name string>PROTOCOL=<server protocol float>. The communication path may be (1)entity sends this packet to the ASR router 202 and (2) ASR router 202responds with ACK packet with the following format: Packet type=TEXT,and Data=ACK\0.

Another of a plurality of router control packets may be an alter routerlogging format packet. This function may cause the ASR router 202 toread a logging.properties file, and update its logging format duringruntime. This may be useful for debugging purposes. The location of thelogging.properties file may be specified when the ASR router 202 isstarted. The packet format for the alter router logging format may havea specific format, such as Packet type=ROUTER_CONTROL, withData=ReadLogConfigurationFile. The communications path may be (1) entitysends this packet to the ASR router 202 and (2) ASR router 202 respondswith ACK packet with the following format: Packet type=TEXT, andData=ACK\0.

Another of a plurality of router control packets may be a get ASR serverstatus packet. The ASR server 204 may self report the status of thecurrent ASR server 204 with this packet. The packet format for the getASR server 204 status may have a specific format, such as Packettype=ROUTER_CONTROL, with data=RequestStatus\0. The communications pathmay be (1) entity sends this packet to the ASRServer 204 and (2) ASRServer 204 responds with a status packet with the following format:Packet type=TEXT; Data=ASRServerStatus: Status=<1 for ok or 0 for error>AM=<AM id> LM=<LM id> NumSessions=<number of active sessions>NumUtts=<number of queued utterances> TimeSinceLastRec=<seconds sincelast recognizer activity>\n Session: client=<client id> speaker=<speakerid> sessioncount=<sessioncount>\n<other Session: line if other sessionsexist>\n \0. This router control packet may be used by the ASR router202 when establishing whether or not an ASR server 204 is indeedfunctional.

There may be a plurality of message packets associated withcommunications between the ASR client 118, ASR router 202, and ASRservers 204, such as error, warning, and status. The error messagepacket may be associated with an irrecoverable error, the warningmessage packet may be associated with a recoverable error, and a statusmessage packet may be informational. All three types of messages maycontain strings of the format:“<messageType><message>message</message><cause>cause</cause><code>code</code></messageType>”.“messageType” is one of either “status,” “warning,” or “error.”“message” is intended to be displayed to the user, “cause” is intendedfor debugging, and “code” is intended to trigger additional actions bythe receiver of the message.

The error packet may be sent when a non-recoverable error occurs and isdetected. After an error packet has been sent, the connection may beterminated in 5 seconds by the originator if not already closed by thereceiver. The packet format for error may have a specific format, suchas Packet type=MESSAGE; and Data=“<error><message>errormessage</message><cause>error cause</cause><code>errorcode</code></error>”. The communication path from ASR client 118 (theoriginator) to ASR server 204 (the receiver) may be (1) ASR client 118sends error packet to ASR server 204, (2) ASR server 204 should closeconnection immediately and handle error, and (3) ASR client 118 willclose connection in 5 seconds if connection is still live. There are anumber of potential causes for the transmission of an error packet, suchas the ASR has received beginning of stream (BOS), but has not receivedend of stream (EOS) or any waveform packets for 20 seconds; a client hasreceived corrupted data; the ASR server 204 has received corrupted data;and the like. Examples of corrupted data may be invalid packet type,checksum mismatch, packet length greater than maximum packet size, andthe like.

The warning packet may be sent when a recoverable error occurs and isdetected. After a warning packet has been sent, the current requestbeing handled may be halted. The packet format for warning may have aspecific format, such as Packet type=MESSAGE;Data=“<warning><message>warning message</message><cause>warningcause</cause><code>warning code</code></warning>”. The communicationspath from ASR client 118 to ASR server 204 may be (1) ASR client 118sends warning packet to ASR server 204 and (2) ASR server 204 shouldimmediately handle warning. The communications path from ASR server 204to ASR client 118 may be (1) ASR server 204 sends error packet to ASRclient 118 and (2) ASR client 118 should immediately handle warning.There are a number of potential causes for the transmission of a warningpacket; such as there are no available ASR servers 204 to handle therequest ModelID because the ASR servers 204 are busy.

The status packets may be informational. They may be sent asynchronouslyand do not disturb any processing requests. The packet format for statusmay have a specific format, such as Packet type=MESSAGE;Data=“<status><message>status message</message><cause>statuscause</cause><code>status code</code></status>”. The communications pathfrom ASR client 118 to ASR server 204 may be (1) ASR client 118 sendsstatus packet to ASR server 204 and (2) ASR server 204 should handlestatus. The communication path from ASR server 204 to ASR client 118 maybe (1) ASR server 204 sends status packet to ASR client 118 and (2) ASRclient 118 should handle status. There are a number of potential causesfor the transmission of a status packet, such as an ASR server 204detects a model ID change for a waveform, server timeout, server error,and the like.

The elements depicted in flow charts and block diagrams throughout thefigures imply logical boundaries between the elements. However,according to software or hardware engineering practices, the depictedelements and the functions thereof may be implemented as parts of amonolithic software structure, as standalone software modules, or asmodules that employ external routines, code, services, and so forth, orany combination of these, and all such implementations are within thescope of the present disclosure. Thus, while the foregoing drawings anddescription set forth functional aspects of the disclosed systems, noparticular arrangement of software for implementing these functionalaspects should be inferred from these descriptions unless explicitlystated or otherwise clear from the context.

Similarly, it will be appreciated that the various steps identified anddescribed above may be varied, and that the order of steps may beadapted to particular applications of the techniques disclosed herein.All such variations and modifications are intended to fall within thescope of this disclosure. As such, the depiction and/or description ofan order for various steps should not be understood to require aparticular order of execution for those steps, unless required by aparticular application, or explicitly stated or otherwise clear from thecontext.

The methods or processes described above, and steps thereof, may berealized in hardware, software, or any combination of these suitable fora particular application. The hardware may include a general-purposecomputer and/or dedicated computing device. The processes may berealized in one or more microprocessors, microcontrollers, embeddedmicrocontrollers, programmable digital signal processors or otherprogrammable device, along with internal and/or external memory. Theprocesses may also, or instead, be embodied in an application specificintegrated circuit, a programmable gate array, programmable array logic,or any other device or combination of devices that may be configured toprocess electronic signals. It will further be appreciated that one ormore of the processes may be realized as computer executable codecreated using a structured programming language such as C, an objectoriented programming language such as C++, or any other high-level orlow-level programming language (including assembly languages, hardwaredescription languages, and database programming languages andtechnologies) that may be stored, compiled or interpreted to run on oneof the above devices, as well as heterogeneous combinations ofprocessors, processor architectures, or combinations of differenthardware and software.

Thus, in one aspect, each method described above and combinationsthereof may be embodied in computer executable code that, when executingon one or more computing devices, performs the steps thereof. In anotheraspect, the methods may be embodied in systems that perform the stepsthereof, and may be distributed across devices in a number of ways, orall of the functionality may be integrated into a dedicated, standalonedevice or other hardware. In another aspect, means for performing thesteps associated with the processes described above may include any ofthe hardware and/or software described above. All such permutations andcombinations are intended to fall within the scope of the presentdisclosure.

While the invention has been disclosed in connection with the preferredembodiments shown and described in detail, various modifications andimprovements thereon will become readily apparent to those skilled inthe art. Accordingly, the spirit and scope of the present invention isnot to be limited by the foregoing examples, but is to be understood inthe broadest sense allowable by law.

All documents referenced herein are hereby incorporated by reference.

What is claimed is:
 1. A method of entering text into a softwareapplication resident on a mobile communication facility comprising:recording speech presented by a user using a mobile communicationfacility resident capture facility; transmitting the recording through awireless communication facility to a speech recognition facility,wherein an automated speech recognition router is configured to providea connection between the wireless communication facility, the speechrecognition facility, and a user database and wherein the automatedspeech recognition router is further configured to select the speechrecognition facility from one or more available speech recognitionfacilities, wherein the automated speech recognition router isconfigured to stream one or more waveform packets to an automated speechrecognition server and to cache the speech presented by the user andsession information at the automated speech recognition router;transmitting information relating to the software application to thespeech recognition facility; selecting at least one statistical languagemodel from a plurality of language models; generating results utilizingthe speech recognition facility using the at least one statisticallanguage model based at least in part on the information relating to thesoftware application and the recording; transmitting the results to themobile communications facility; loading the results into the softwareapplication; and adapting the speech recognition facility based onusage.
 2. The method of claim 1, wherein adapting the speech recognitionfacility based on usage includes at least one of adapting an acousticmodel, adapting a set of pronunciation, adapting a vocabulary, andadapting a language model.
 3. The method of claim 1, wherein, adaptingthe speech recognition facility includes adapting recognition modelsbased on usage data.
 4. The method of claim 1, wherein the at least onestatistical language model is a large vocabulary statistical languagemodel.
 5. The method of claim 1, further comprising combining wordsrecognized from more than one statistical language model to generate theresults.
 6. The method of claim 1, further comprising a subsequent stepof generating results in the speech recognition facility based onresults obtained in a previous step of generating results.
 7. The methodof claim 3, wherein adapting recognition models makes use of humantranscriptions of speech of the user.
 8. The method of claim 1, whereinthe plurality of language models is an initial set of language modelsselected based on the information relating to the software applicationand the recording.
 9. The method of claim 3, wherein adaptingrecognition models is specific to the user or groups of users.
 10. Themethod of claim 3, wherein adapting recognition models is specific tothe software application or groups of software applications.
 11. Themethod of claim 1, further comprising using manual input of wordsrepresenting the user's speech to improve the statistical languagemodel.
 12. The method of claim 1, wherein the information relating tothe software application includes at least one of an identity of theapplication, an identity of a text box within the application,contextual information within the application, an identity of the mobilecommunication facility, and an identity of the user.
 13. The method ofclaim 1, further comprising deciding whether the selected at least onestatistical language model provides insufficient recognition output andselecting at least one other language model based on the speechrecognized by the selected at least one statistical language model. 14.The method of claim 1, wherein the speech recognition facility includesat least one of an acoustic model, a set of pronunciations, and avocabulary.
 15. The method of claim 1, wherein the at least onestatistical language model is selected based on the information relatingto the software application and the recording.
 16. The method of claim1, further comprising selecting a different at least one statisticallanguage model based on the results generated.
 17. The method of claim1, further comprising displaying a correction interface for the user tocorrect or change the results.
 18. The method of claim 1, furthercomprising running a plurality of language models in multiple passes inthe speech recognition facility.
 19. The method of claim 8, wherein theinitial set of language models includes a general language model for asearch, a general language model for date and time entry, and a generallanguage model for digit string entry.
 20. The method of claim 8,wherein the initial set of language models is tailored to a group towhich the user belongs.
 21. The method of claim 18, wherein the outputsof the multiple passes in the speech recognition facility are combinedinto a single result by a merging of results from the multiple passes.22. The method of claim 21, wherein the merging of results is at a wordlevel.
 23. The method of claim 21, wherein the merging of results isdone at a phrase level.
 24. A method of entering text into a softwareapplication resident on a mobile communication facility comprising:recording speech presented by a user using a mobile communicationfacility resident capture facility; transmitting the recording through awireless communication facility to a speech recognition facility,wherein an automated speech recognition router is configured to providea connection between the wireless communication facility, the speechrecognition facility, and a user database and wherein the automatedspeech recognition router is further configured to select the speechrecognition facility from one or more available speech recognitionfacilities, wherein the automated speech recognition router isconfigured to stream one or more waveform packets to an automated speechrecognition server and to cache the speech presented by the user andsession information at the automated speech recognition router;transmitting information relating to the software application to thespeech recognition facility; selecting an initial set of a plurality oflanguage models for processing the recorded speech; generating resultsutilizing the speech recognition facility independent of a structuredgrammar based at least in part on the information relating to thesoftware application and the recording; transmitting the results to themobile communications facility; and loading the results into thesoftware application.
 25. The method of claim 24, wherein at least oneof the initial set of the plurality of language models is a statisticallanguage model.
 26. The method of claim 24, further comprising decidingwhether the selected initial set provides insufficient recognitionoutput and selecting at least one other language model based on thespeech recognized by the selected initial set.
 27. The method of claim24, further comprising adapting the speech recognition facility based onusage.